Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning

TL;DR

Gym-Ignition introduces a flexible, modular, and reproducible simulation framework for robotic reinforcement learning, supporting multiple engines, distributed computing, and seamless integration with existing RL tools.

Contribution

It provides a new framework that enhances reproducibility, modularity, and scalability of robotic simulations for reinforcement learning research.

Findings

Supports multiple physics and rendering engines as plugins

Enables distributed simulation across multiple machines

Provides a Python interface compatible with OpenAI Gym

Abstract

This paper presents Gym-Ignition, a new framework to create reproducible robotic environments for reinforcement learning research. It interfaces with the new generation of Gazebo, part of the Ignition Robotics suite, which provides three main improvements for reinforcement learning applications compared to the alternatives: 1) the modular architecture enables using the simulator as a C++ library, simplifying the interconnection with external software; 2) multiple physics and rendering engines are supported as plugins, simplifying their selection during the execution; 3) the new distributed simulation capability allows simulating complex scenarios while sharing the load on multiple workers and machines. The core of Gym-Ignition is a component that contains the Ignition Gazebo simulator and exposes a simple interface for its configuration and execution. We provide a Python package that allows developers to create robotic environments simulated in Ignition Gazebo. Environments expose the common OpenAI Gym interface, making them compatible out-of-the-box with third-party frameworks containing reinforcement learning algorithms. Simulations can be executed in both headless and GUI mode, the physics engine can run in accelerated mode, and instances can be parallelized. Furthermore, the Gym-Ignition software architecture provides abstraction of the Robot and the Task, making environments agnostic on the specific runtime. This abstraction allows their execution also in a real-time setting on actual robotic platforms, even if driven by different middlewares.